G protein coupled receptors (GPCRs) are a superfamily of proteins that are activated by a wide range of natural ligands, for instance neurotransmitters and hormones, and are targeted by many of the marketed and leisure drugs. Given their pharmaceutical relevance, there is great interest in the experimental elucidation or the modeling of their three-dimensional (3D) structures, which can be employed as platforms to discover or design new ligands that can modulate their activity. Through this proposal, we aim at furthering the field of virtual screening applied to ho ology models of G proteincoupledreceptors (GPCRs), thus enabling the scientific community to harness the knowledge deriving from the blossoming GPCR structural studies and expand it to the other superfamily members (1-4). In particular we are focusing on the largest class of GPCRs (class A, also known as family I or rhodopsin family), which comprises about 84% of the entire superfamily. Since class A GPCRs are proteins of very high pharmaceutical relevance, our research will ultimately provide tools that facilitate the rational discovery of drugs acting through this prominent class of targets. Though my career, I have been at the forefront of GPCR modeling. In 2008, with a single author article, I was the first to conclusively demonstrate that accurate GPCR homology models can be constructed (1). Later the same year, my structures resulted to be the most accurate of all those submitted to the first blind assessment of GPCR modeling and docking (2). Hence, I am in a very good position to conduct the proposed research. A substantial set of preliminary data for the research proposed here stem from a systematic study on GPCR homology modeling that I conducted at American University with a large cohort of research students. The study describes the construction of models of the ?2-adrenergic receptor in the inactive state, using as templates all the other class A GPCRs for which structures that reflect the inactive state have been solved. One of the key results of the study is the linear correlation that we found between the structural accuracy of the models and the sequence identity between modeled receptor and templates. This result suggests that, for a given receptor, the accuracy of the attainable models can be predicted on the basis of the available templates. As outlined below, we aim at furthering our investigation through two specific aims. Specific aims of this proposal include: 1) Probing the applicability of Class A GPCR models to virtual screening for the identification of blockers and delineating best practices; 2) Probing the applicability of Class A GPCR models to virtual screening for the identification of agonists and delineating best practices. A further objective of this study is to strengthen the research environment at American University and give our students the opportunity to conduct research and receive training in the field of molecular modeling.